Astrophysics/Machine Learning PhD Researcher

Technical Skills

• Programming Languages: Python, Java, SQL
• Data Containers: pandas, h5py
• ML frameworks: scikit-learn, PyTorch, pytorch-lightning, Pyro

Education

• PhD Astrophysics, University of Surrey (Sept 2023 - Current)
• MSc. Computer Science, Swansea University (2021 - 2022)
• MSci. Physics with Particle Physics and Cosmology, University of Birmingham (2016 - 2020)

Work Experience

• Freelance Data Analyst, TELUS (Aug 2022 - Aug 2022)
• Research Intern, Particle Physics group, University of Birmingham (Summer 18' and 19')

Current Projects

Bayesian Neural Networks for Predicting the Time Evolution of Supermassive Black Hole Binaries (Ongoing)

Research Interests

My astrophysical interests include: N-body simulations, Supermassive black holes and stochastic gravitational wave background. In tandem, I'm very much interested in SciML, and how it can be applied to astrophysics to build interpretable surrogate models from simulations. The current techniques that I'm particularly fond of are: Bayesian Deep Learning, Physics-informed Neural Networks, Neural ODEs and Variational inference.

Other Interests

• I've also enjoy experimenting with foundation models on HuggingFace. Recently experimented with RAG using Cohere API + LlamaIndex + LangChain.
• I'm currently trying to pick up Julia so that I can use their SciML ecosystem.

My Current Setup

• bash + iTerm2 + Oh-My-Bash
• VS Code + essential Python extensions for structured Python projects
• Jupyter notebooks for experimenting in Python/Julia
• Cookiecutter for generating project templates
• I tend to follow the PEP 8 Style Guide and the Zen of Python